Detecting where an object's three-dimensional shape has changed requires both the ability to model shape from images taken at different times and the ability to distinguish significant from insignificant differences in the models derived from two sets.
Change detection is an important task in computer vision that has been addressed early at the image intensity level. However, comparing intensity values is not very effective because such changes do not necessarily reflect actual changes in shape, but could be caused by changes in viewing and illumination conditions or even in reflectance (e.g., seasonal changes). Although it has been attempted, this is not easy to take them into account at this level. For manmade objects such as buildings, higher-level comparisons have been proposed, based on feature organization and 3-D modeling. These specialized approaches are the most successful, but are not applicable to more general objects like natural terrain.
A few of the ideas needed for general change detection in shape are found in other areas of computer vision. In work on tracking, statistics have been computed during a learning phase and then used to differentiate between significant and insignificant changes. The problem is simplified by the fact that the camera is stationary, whereas it is desired to deal with various viewpoints.
Accordingly, a need exists for a more effective method for detecting change in three-dimensional objects. Also, a need exists for a method that can satisfy the above needs and that is cost effective and not overly expensive.